Predicting orthosomnia in an individual

ABSTRACT

A mechanism for identifying orthosomnia, being the preoccupation with sensor-derived sleep measures or automated sleep analysis processes, within an individual. Interactions between the individual and a software application performing a sleep analysis process are monitored and used to generate a predictive indicator that indicates a likelihood that the individual has orthosomnia. This predictive indicator may be used to control the software application, and in particular, to control the display of information provided by the software application.

CROSS-REFERENCE TO PRIOR APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.63/054,197, filed on 20 Jul. 2020. This application is herebyincorporated by reference herein.

FIELD OF THE INVENTION

The present invention relates to the field of sleep analysis, and inparticular, to the field of sleep analysis performed by an individualwithout clinician input.

BACKGROUND OF THE INVENTION

The use of (wearable) sleep trackers is being more popular andwidespread, and are often used to perform sleep analysis or assessmentwithout the input of a clinician/physician.

Sleep trackers often execute a software application, running on theprocessor, to perform a sleep analysis or assessment process, e.g. basedupon biometric data gathered by the sleep tracker itself or an externalsensor, such as movement data from an accelerometer (e.g. mounted on theindividual's wrist) or heartrate data from a heartrate monitor.

However, despite their widespread use, the accuracy of sleep trackershas been brought into question. Studies evaluating the ability ofwearable trackers to estimate sleep have generally found highsensitivity, comparatively low specificity, and invalidity around waketimes during sleep. Additionally, even when users are presented withaccurate sleep tracking data within a healthy normal range, users maylack the domain knowledge to appropriately interpret the informationprovided to them.

Moreover, a disconnect between sleep quality information provided by asleep tracker and a subjective analysis of sleep quality performed bythe individual has been observed. This is at least partly because anindividual's subjective evaluation of their sleep quality is largelydependent on sleep onset latency (SOL) and the frequency and duration ofwakefulness during the night, estimates of which vary from objectivemeasures obtained using a sleep tracker.

This disconnect can cause anxiety and concern in an individual who usesa sleep tracker, and there is an ongoing desire to address this problem.

SUMMARY OF THE INVENTION

The invention is defined by the claims.

According to examples in accordance with an aspect of the invention,there is provided a computer-implemented method for generating apredictive indicator that indicates a likelihood of orthosomnia in anindividual.

The computer-implemented method comprises: obtaining user interactiondata, being data responsive to the individual's interactions, via a userinterface for a processor, with a software application running on theprocessor for performing a sleep analysis or assessment process; andprocessing the obtained user interaction data to generate the predictiveindicator that indicates a likelihood of orthosomnia in the individual.

Orthosomnia is a condition characterized by a preoccupation or obsessionwith improving or perfecting sleep quality data generated by a(computer-executed) sleep analysis or assessment process, and inparticular to sensor-derived sleep quality data (being a prediction ofsleep quality of the individual generated based onbiometric/physiological data of the individual).

The present disclosure recognizes that a user interaction with asoftware application running or performing a sleep analysis/assessmentprocess can be indicative of an individual's preoccupation with datagenerated by the software application. In particular, a manner in whicha user interacts with the software application, or the information thatis supplied to the software application by the user, is indicative ofpossible orthosomnia in the individual.

The present disclosure proposes an approach for identifying possibleorthosomnia in the individual, by assessing/analyzing user interactiondata responsive to the user's interaction with the software application.The user's interaction is via a user interface, which may be formed inthe same device that comprises the processor that carries out or runsthe software application (e.g. a mobile/cellular phone) or a separatedevice (e.g. the processor is a distributed processing system and theuser interface is a mobile/cellular phone in communication with thedistributed processing system).

In one example, the computer-implemented method comprises displaying thepredictive indictor via the user interface. This provides anindividual/clinician with additional information to aid them inperforming a treatment/diagnostic process.

In some examples, the user interaction data comprises access dataresponsive to the individual accessing and/or opening the softwareapplication. This embodiment recognizes that the characteristics of theuser accessing or opening the software application are indicative ofwhether or not individual has orthosomnia.

For instance, the access data may comprise one or more measures ofaccessing and/or opening frequency and/or duration of access. Thisembodiment recognizes a link between the amount of time that a useraccesses the software application (and/or a frequency of access) andlikelihood of orthosomnia. In particular, it is recognized that anindividual who frequently accesses the software application, or accessesthe software application for a long period of time, is more likely tohave orthosomnia than an individual who less frequently, or for shorterperiods of time, accesses the software application.

Frequent and/or long accesses thereby indicate a high likelihood oforthosomnia. Low and/or short accesses similarly indicate a lowlikelihood of orthosomnia.

The one or more measures may relate to a time or frequency of accessingparticular parts, screens or displays of the software application, e.g.a part that displays sleep quality data. It is recognized that apreoccupation with certain elements of the software application (such asthe quality data) may be indicative of potential orthosomnia.

In some examples, the step of processing the obtained user interactiondata comprises: obtaining population access data, being data that isresponsive to population trends of other individual's interactions withthe software application running on the processor and/or one or moreother versions of the software application running on one or more otherprocessors; and comparing the access data to the population access datato generate the predictive indicator.

The population access data may thereby provide a benchmark, baseline oraverage expected level of accesses to the software application. Inparticular, a population trend of other individual's interactions withthe software application (or other instances/versions of the softwareapplication) provides information establishing a baseline against whicha likelihood of orthosomnia can be predicted. For instance, a strong(positive) deviation from a mean of a population could indicate theindividual is accessing the software application more commonly thanexpected, indicating possible orthosomnia.

This approach thereby provides an accurate mechanism for assessing alikelihood of orthosomnia in the individual.

The access data may comprise time data indicating a time (of day) atwhich the individual interacts with the software application. The timedata may, for example, comprise one or more time stamps or other similardata structures.

In some examples, this time data may be processed by itself to generatethe predictive indicator. This embodiment recognizes that a time atwhich an individual interacts with the software application (as a whole,or with certain parts of the software application) may be indicative ofpossible orthosomnia. For instance, an individual is more likely to haveorthosomnia if they interact with the software application during thenight (e.g. as this would demonstrate an anxiety about their quality ofsleep) or repeatedly throughout the day (e.g. as this would indicate apreoccupation with their purported quality of sleep).

In some examples, the step of processing the obtained user interactiondata comprises: obtaining, from a biometric sensor, biometric data ofthe user, the biometric data being responsive to changes in one or morephysiological parameters of the individual at or during a time at whichthe individual accesses and/or opens the software application, asindicated by the time data; and processing the biometric data togenerate the predictive indicator.

This embodiment recognizes that a physiological response of theindividual during an accessing of the software application is indicativeof potential orthosomnia. For instance, if the individual reactsnervously or negatively during access of the software application, thiscould indicate potential orthosomnia.

The one or more physiological parameters of the individual comprise aphysiological parameter responsive to an autonomic response of theindividual, such as a heartrate, sweating, a temperature, a respiratoryrate, skin color, eye movement and/or an eye dilation. These embodimentsrecognize that an involuntary response (autonomic response) may indicatea nervousness or concern with the information provided by the softwareapplication, and thereby indicates a likelihood of orthosomnia. Theproposed approach thereby provides a user independent mechanism, i.e. anobjective mechanism, for predicting a likelihood of orthosomnia.

In at least one embodiment, the step of processing the obtained userinteraction data comprises: obtaining historic access data, being dataresponsive to the individual's historic interactions, via the userinterface, with the software application; and comparing the access datato the historic access data to generate the predictive indicator.

A change in how a user accesses or opens the software application overtime may indicate a change in potential orthosomnia. For instance, if auser is accessing the software application more frequently than in thepast, then this may indicate that the probability of the user havingorthosomnia is increasing.

In some examples, the step of obtaining the user interaction datacomprises obtaining, via the user interface, user-derived sleep qualitydata representing a subjective perception of sleep quality of theindividual.

The step of processing the obtained user interaction data may comprise:obtaining, from a sleep sensor, sleep sensor data responsive to one ormore changes of physiological parameters of the individual during theindividual's sleep; processing the sleep sensor data to generatesensor-derived sleep quality data, being data representing an objectivemeasure of the sleep quality of the individual; and comparing theuser-derived sleep quality data to the sensor-derived sleep quality datato generate the predictive indicator.

This embodiment recognizes that a difference between sensor-derivedsleep quality data and user-derived sleep quality data indicatespotential orthosomnia or an increased risk of orthosomnia. Inparticular, if there is a large difference between an individual'sperception of their sleep, and the quality of sleep derived from sensordata, then there is an increased likelihood that the individual willbecome anxious about the quality data produced by the softwareapplication—which may distort their own perception of their sleepquality, leading to orthosomnia.

There is also proposed a computer-implemented method of controlling asoftware application running on a processor for performing a sleepanalysis or assessment process.

The computer-implemented method comprises: generating a predictiveindicator that indicates a likelihood of orthosomnia in an individual byperforming any suitable herein described method; and controlling thesoftware application to modify and/or supplement information presentedto the individual, via the user interface, during the sleep analysis orassessment process based on the predictive indicator.

This approach recognizes that the effects of orthosomnia can bemitigated or reduced by controlling the information presented to theindividual (at the user interface) by the software application. Throughappropriate control of the software application, the individual can beprovided with suitable information to assuage their concerns oranxieties and/or reduce the likelihood of orthosomnia in the individual.

The skilled person would appreciate that, in another aspect of theinventive concept, the method of generating the predictive indicator(for the method of controlling the software application) may be carriedout by a process not disclosed in this specification. For instance, thepredictive indicator may be an indicator received from a clinician(indicating a diagnosis of orthosomnia) and/or the individual (e.g. ifthey are concerned they may have orthosomnia).

In some examples, the software application is configured to generate anddisplay, at the user interface, a sleep quality measure during the sleepanalysis or assessment process.

The step of controlling the software application may comprise modifyingthe sleep quality measure in response to the predictive indicatorindicating that a likeliness of orthosomnia in the individual fallswithin a first predetermined range.

The step of controlling the software application may comprisesuppressing the display of the sleep quality measure in response to thepredictive indicator indicating that a likeliness of orthosomnia fallswithin a second predetermined range, which may be different to the firstpredetermined range (if present).

The step of controlling the software application may comprise providing,at the display, supplementary information about sleep quality measuresin response to the predictive indicator indicating that a likeliness oforthosomnia falls within a third predetermined range, which may bedifferent to the first and/or second predetermined range (ifpresent)—but may overlap the first and/or second predetermined range inother examples.

Any combination of these approaches could be used. These embodimentsrecognize that the information presented by the software application mayaffect a likelihood the individual having orthosomnia. Bychanging/suppressing/supplementing the information, a level of controlcan be exerted over the likelihood of the individual having orthosomnia,e.g. reducing a likelihood or intensity of the individual's orthosomnia.

In some embodiments, the step of obtaining the user interaction datacomprises obtaining, via the user interface, user-derived sleep qualitydata representing a subjective perception of sleep quality of theindividual.

The computer-implemented method may further comprise: obtaining, from asleep sensor, sleep sensor data responsive to one or more changes ofphysiological parameters of the individual during the individual'ssleep; and processing the sleep sensor data to generate sensor-derivedsleep quality data, being data representing an objective measure of thesleep quality of the individual, wherein the supplementary informationcomprises information responsive to a difference between theuser-derived sleep quality data and the sensor-derived sleep qualitydata.

It is recognized that explanative information that can explain orjustify a difference between an individual's perception of their sleep,and a software applications prediction of the quality of their sleep,can help reduce the likelihood and/or intensity of orthosomnia in theindividual. This is because the user is provided with information thataids them in their understanding of their clinical state, and thereforereduces the likelihood of the user developing, or furthering,orthosomnia.

In some examples, the computer-implemented method is performed by thesoftware application. Of course, the computer-implemented method couldbe performed by a different software application (e.g. supplementary tothe software application).

In some examples, the predictive indicator comprise a binary indicatorindicating a prediction of whether or not the individual hasorthosomnia. A binary indicator is a data point having only two possiblevalue. In other examples, the predictive indicator comprises acategorical indicator (e.g. data having a discrete or finite number ofpossible values), a continuous indicator (e.g. data having a non-finitenumber of possible values—or the maximum number of possible values for aparticular computing environment) or a numerical indicator (e.g. aprobability).

There is also proposed a computer program product comprising computerprogram code means which, when executed on a computing device having aprocessing system, cause the processing system to perform all of thesteps of any herein described method.

There is also proposed a processing system for generating a predictiveindicator that indicates a likelihood of orthosomnia in an individual.The processing system is configured to obtain user interaction data,being data responsive to the individual's interactions, via a userinterface for a processor, with a software application running on theprocessor for performing a sleep analysis or assessment process; andprocess the obtained user interaction data to generate the predictiveindicator that indicates a likelihood of orthosomnia in the individual.

The skilled person would be readily capable of adapting any hereindescribed processing system to carry out any herein described method andvice versa.

These and other aspects of the invention will be apparent from andelucidated with reference to the embodiment(s) described hereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of the invention, and to show more clearlyhow it may be carried into effect, reference will now be made, by way ofexample only, to the accompanying drawings, in which:

FIG. 1 illustrates a processing arrangement;

FIG. 2 is a flowchart illustrating a method;

FIG. 3 is a flowchart illustrating a method; and

FIG. 4 illustrates a processing system.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The invention will be described with reference to the Figures.

It should be understood that the detailed description and specificexamples, while indicating exemplary embodiments of the apparatus,systems and methods, are intended for purposes of illustration only andare not intended to limit the scope of the invention. These and otherfeatures, aspects, and advantages of the apparatus, systems and methodsof the present invention will become better understood from thefollowing description, appended claims, and accompanying drawings. Itshould be understood that the Figures are merely schematic and are notdrawn to scale. It should also be understood that the same referencenumerals are used throughout the Figures to indicate the same or similarparts.

The invention provides a mechanism for identifying orthosomnia, beingthe preoccupation with sensor-derived sleep measures or automated sleepanalysis processes, within an individual. Interactions between theindividual and a software application performing a sleep analysisprocess are monitored and used to generate a predictive indicator thatindicates a likelihood that the individual has orthosomnia. Thispredictive indicator may be used to control the software application,and in particular, to control the display of information provided by thesoftware application.

Embodiments are based on the realization that interactions between theindividual and the software application, and in particular the manner inwhich the individual interacts with the software application, isindicative of whether or not the individual has orthosomnia. In thisway, a predictive indicator can be generated to aid in the treatmentand/or diagnosis of the individual, i.e. act as an aid to a clinicaldecision making process.

This proposed concepts aid in the identification, management and reliefof orthosomnia.

The increasing use of wearable sleep tracking devices allows individualsto monitor sleep patterns objectively at a consumer level. However, withaccess to this objective, albeit often inaccurate, data—“sensor-derivedsleep quality data”—some individuals can (mistakenly, butunintentionally) identify and self-diagnose sleep disturbances, forwhich they proceed to seek medical treatment. Additionally, individualsmay lack the knowledge to appropriately interpret sensor-derived sleepquality data as being within a healthy spectrum or as being indicativeof a sleep disorder. For a subset of individuals, the use of sleeptrackers has unintended, adverse effects, as individuals attempt toincrease sleep scores or eliminate undesired disturbances in their datathrough methods which often reinforce poor sleep habits, includingspending excessive time in bed (causing a decrease in sleep efficiency)in order to attempt to increase sleep duration. There is a recognizedcondition of orthosomnia, being the preoccupation with improving orperfecting sleep data. Orthosomnia can result in a harmful negativefeedback loop as an individual's stress about their sleep causes theirsleep quality to further deteriorate.

This condition was recognized by K. G. Baron et al in the journalarticle Baron, K. G., Abbott, S., Jao, N., Manalo, N. and Mullen, R.,2017. Orthosomnia: Are some patients taking the quantified self toofar?. Journal of Clinical Sleep Medicine, 13(2), pp. 351-354.

In some cases of orthosomnia, individuals only identify perceived sleepissues after monitoring sensor-derived sleep quality data and findinginstances of reported restlessness or low sleep efficiency, of whichthey were never previously aware, or due to lack of familiarity with asleep scoring system. To individuals having orthosomnia, sensor-derivedsleep quality data can feel more consistent with their experience ofsleep than validated techniques, such as polysomnography or actigraphy.As a result, individual's perceptions can prove difficult to alter. Thiscan negatively affect treatment because individuals can be reluctant tomake changes that would decrease or affect their sensor-derived sleepquality data, such as refusing to try sleep restriction (or sleepconsolidation therapy) because it would temporarily lower sleepduration. This is compounded by the individual's awareness of the lackof trust by clinician in sensor-derived sleep quality data, whichaffects the confidence that the individual may have in the clinician.

This invention is intended to provide approaches for predicting alikelihood of orthosomnia, and proposes a number of approaches formanaging and relieving orthosomnia in an individual.

In particular examples, approaches facilitate improved understanding (byat least the individual) of the sensor-derived sleep quality data,placing value on their subjective sleep experience rather than solelyrelying on objective reports of potentially questionable accuracy.

FIG. 1 illustrates a processing arrangement 100 in which embodiments maybe employed, for the purposes of improved contextual understanding.

The processing arrangement 100 comprises a user interface 110, e.g.comprising a display and an input interface. The user interface 110 isable to communicate with a processor 120. The user interface 110 and theprocessor 120 may, as illustrated, be housed in a same device 105 (e.g.a smartphone, a smartwatch, a laptop and so on). In other examples, theuser interface 110 and the processor 120 are housed separately. Forinstance, the processor 120 may be a distributed or cloud-computingsystem.

The processor 120 is configured to run, operate or execute a softwareapplication running for performing a sleep analysis or assessmentprocess. Typically, a sleep analysis process comprises obtainingbiometric data of the individual, e.g. from a sensor 130, and processingthe biometric data to generate sensor-derived sleep quality data. Thesensor-derived sleep quality data represents an objective measure of thequality of the individual's sleep, such as a sensor-derived qualitymeasure score (i.e. a “sleep quality measure”), a total sleep time, anumber of sleep interruptions, length of deep sleep (e.g. non-REM sleep)and so on.

In some examples, processing the biometric data to generate thesensor-derived sleep quality data may involve smoothing areas known tobe unreliable according to wearable data or areas known to be habituallyrelevant to the individual. For example, if it is known that the usertypically reads for one hour before bed, their total sleep time (TST) islikely to be overestimated by the sensor-derived sleep quality data andshould be adjusted.

In some examples, additional information may be used in the generationof the sensor-derived sleep quality data. The additional informationmay, for example, comprise user information, such as an age, gender,health conditions and diagnosed sleep disorders and/or typical/historicsleep patterns of the user. The additional information may compriseinformation about the sleep sensor and/or software application (e.g. totake account of any known measuring errors or inconsistencies) and/orinformation about the sleep environment (such as a noise level, lightlevel, etc.).This additional information may be provided by a user atthe user interface 110.

Approaches for generating or deriving quality data from biometric dataof an individual (obtained at a sensor 130), and optionally additionalinformation, are well known, and are not further described for the sakeof conciseness. Approaches may comprise using a machine-learningalgorithm (e.g. a neural network, a naïve Bayesian classifier or asupport-vector machine) to generate the sensor-derived sleep qualitydata from the biometric data.

Some suitable examples of generating sensor-derived sleep quality datacan be found in the US patents having publication numbers U.S. Ser. Nos.10/624,574 and 10/524,674, or the US patent applications havingpublication numbers U.S. Ser. Nos. 16/495,687 and 16/589,181. Othersuitable approaches will be well known.

The processor 120 may be adapted to control the user interface 110 todisplay a visual representation of the sensor-derived sleep qualitydata. In particular, the individual may be able to access or open thesoftware application, and view the visual representation of thesensor-derived sleep quality data.

Thus, the software application is configured so that an individual isable to access and/or view the sensor-derived sleep quality data.

The sensor 130, i.e. a “sleep sensor”, is any sensor that is able tomonitor one or more physiological parameters indicative of a change in auser's quality of sleep. For instance, the physiological parameter(s)may include one or more of: a heartrate; a movement; a respiratory rate;brain wave activity and so on. The sensor 130 may be formed in the samedevice 105 as the user interface 110 and/or the processor 120.Preferably, at least the sensor 130 is wearable by the user, forexample, the whole device 105 may be wearable.

Other suitable configurations for a processing arrangement 100 will bereadily understood by the skilled person, e.g. employing externalservers, external processing systems or communications channels betweendifferent aspects of the processing arrangement.

FIG. 2 illustrates a computer-implemented method 200 according to anembodiment. The method 200 is configured for generating a predictiveindicator that indicates a likelihood of orthosomnia in an individual,and may be carried out by a processing system according to anembodiment. In some examples, the computer-implemented method isperformed by the software application (executed on the processor 120 ofFIG. 1).

The method 200 comprises a step 210 of obtaining user interaction data215. The user interaction data is data responsive to the individual'sinteractions, via a user interface for a processor, with a softwareapplication running on the processor for performing a sleep analysis orassessment process.

The user interaction data may comprise access data, being dataresponsive to the user accessing and/or opening the softwareapplication. The access data may comprise one or more numerical valuesresponsive to a user accessing and/or opening the software application,e.g. where a value represents a time of accessing/opening (e.g. atimestamp or the like), a frequency of accessing/opening (e.g. how manyaccesses/openings per predetermined time period, e.g. per day or in alast predetermined time window, e.g. a last hour or last two hours), acumulative count of accessing/opening, a duration of an access (e.g. inseconds) and so on.

In at least one embodiment, the access data comprises time dataindicating a time (of day) at which the individual interacts with thesoftware application. This may be in the form of one or more timestamps, or other similar measures. In some examples, the access datacomprises one or more measures of accessing and/or opening frequencyand/or duration of access.

For the avoidance of doubt, the “access data” may comprise one or moremeasures of accessing and/or opening frequency and/or duration of accessof one or more parts of the software application (e.g. accessesparticular screens or data available with the software application.

In some examples, the user interaction data comprises user-derived sleepquality data representing a subjective perception of sleep quality ofthe individual, i.e. data that indicates how the individual feels abouttheir sleep quality (preferably without being influenced by thesensor-derived sleep quality data). The user-derived sleep quality datamay, for instance, comprise responses to a survey or a user-providedindication of perceived quality of sleep (e.g. a numeric indicator on apredetermined scale). The user-derived sleep quality data is obtainedfrom a user input provided at the user interface.

In other examples, the user interaction data comprises one or more othermetrics of an interaction between the individual and the softwareapplication. By way of example only, the user interaction data maycomprise one or more values representing a number of times theindividual views a particular piece of data (e.g. a particular screen orthe sensor-derived sleep quality data, e.g. in the form of a sleephypnogram) provided by the software application, a number of link (e.g.to articles) clicked within the software application and so on.

The method 200 then moves to a step 220 of processing the obtained userinteraction data to generate the predictive indicator that indicates alikelihood of orthosomnia in the individual.

In the context of the present invention, a predictive indicator is anydata that changes responsive to changes in a predicted likelihood thatthe individual has orthosomnia. The predictive indicator may comprisebinary, categorical or numerical data. Binary data may indicate aprediction as to whether or not the individual has orthosomnia (e.g. “0”indicates predicted absence and “1” indicate predicted presence or viceversa). Categorical data may indicate a likelihood category (e.g.“Likely”, “Very Likely”, “Unlikely” and so on) that the individual hasorthosomnia. Numerical data may indicate a numeric probability that theindividual has orthosomnia, e.g. on a scale of 0 to 1 or 0 to 100.

The precise mechanism for generating the predictive indicator in step120 may depend upon the content of the user interface data.

In some examples, where the user interaction data comprises access data,the access data may be compared to one or more thresholds to determinewhether or not the individual has orthosomnia. The one or morethresholds may, for example, be derived from population data (e.g.indicating a population average and/or standard deviation for the accessdata).

By way of example, an individual may be considered to have orthosomniaif they access or open the software application for more than 1 minute aday and/or more than 5 days a week. As another example, an individualmay be considered to have orthosomnia if they access or open thesoftware application (e.g. more than a predetermined number of times,e.g. more than 3 times) during a predetermined time window (e.g. whichmay correspond to a time window when the individual is expected to beasleep).

As another example, one or more predetermined thresholds may be setbased on population access data. Thus, step 220 may comprise obtainingpopulation access data, being data that is responsive to populationtrends of other individual's interactions with the software applicationrunning on the processor and/or one or more other versions of thesoftware application running on one or more other processors; andcomparing the access data to the population access data to generate thepredictive indicator.

The population access data may, for instance, define some populationaverage and/or standard deviation.

An individual may be considered to have orthosomnia if their access databreaches some predetermined threshold with respect to population accessdata. For instance, if the access data comprises a value representing afrequency of access (and the population data indicates a population meanand/or standard deviation of frequency of access), the individual may beconsidered to have orthosomnia if their frequency of access is more than2 or 3 standard deviations greater than the population mean average offrequency.

In some examples, a value for the predictive indicator may be set basedon a difference between the access data and the population access data,e.g. a difference between the frequency of access and the average(population) frequency of access.

Other predetermined thresholds and/or approaches for setting thepredetermined thresholds could be apparent to the skilled person. Forinstance, the predetermined thresholds may be set according to someclinical protocol and/or in response to one or moreindividuals/clinicians' input.

In some examples, a machine-learning algorithm could be used to generatethe predictive indicator. The machine-learning algorithm may receive, asinput, the user interaction data (e.g. the access data) and provide, asoutput, the predictive indicator. Examples of suitable machine-learningalgorithms include a neural network, a naïve Bayesian classifier and asupport-vector machine.

As one example, the predetermined thresholds may be set based onhistoric information or access data of the individual. For instance, apredetermined threshold may be based upon an average (mean) and/orstandard deviation of values contained in historic access data, e.g.access data obtained over a previous or past week or month (althoughother time periods are also considered).

More generally, in some examples, the step of processing the obtaineduser interaction data comprises: obtaining historic access data, beingdata responsive to the individual's historic interactions, via the userinterface, with the software application; and comparing the access datato the historic access data to generate the predictive indicator.

In some embodiments, the access data comprises time data indicating atime at which the individual interacts with the software application,e.g. in the form of one or more time stamps. In these examples, the stepof processing the obtained user interaction data may comprise obtainingbiometric data of the user, the biometric data being responsive tochanges in one or more physiological parameters of the individual at orduring a time at which the individual accesses the and/or opens thesoftware application, as indicated by the time data.

The biometric data may then be processed to generate the predictiveindicator, e.g. using predetermined thresholds, comparisons topopulation/historic data and/or machine-learning methods.

In this way, biometrics associated with the utilization of the softwareapplication are used to detect the probability of orthosomnia. Thisembodiment recognizes that a biological response of the individual canindicate whether or not the user has orthosomnia (e.g. based on anassessment as to whether they are nervous/stressed when accessing thesoftware application).

In particular, biometric data may comprise physiological data (i.e.measurements) that represent an unconscious or autonomic response of theindividual to information provided by the software application (e.g. ata user interface). Thus, the physiological data may be responsive to anautonomic response of the individual (during the time indicating by thetime data).

Examples include a heartrate (or other heart characteristics, such as anacceleration of a heartrate), sweating, a temperature, a respiratoryrate, skin color, eye movement and/or an eye dilation. Other suitablephysiological parameters indicative of an autonomic response and/ornervousness/stress of an individual will be apparent to the skilledperson.

The biometric data may be compared to one or more thresholds, e.g. setby a clinician or based on population data, to generate the predictiveindicator. The threshold may be defined to identify an autonomicresponse indicating nervousness or stress when accessing the softwareapplication (herein identified as being a sign/symptom of orthosomnia).For instance, if the biometric data comprises a measured heartrate, thepredictive indicator may indicate that the individual is likely to haveorthosomnia if their measured heartrate exceeds some predeterminedthreshold when accessing the software application. As another example,if the biometric data comprises an eye dilation, the predictiveindicator may indicate that the individual is likely to have orthosomniaif their measured eye dilation (e.g. detectable with a camera or thelike) exceeds some predetermined threshold.

The biometric data may be compared to historic biometric data. Forinstance, if the biometric data comprises a measured respiratory rate,an increase in respiratory rate when the user access the softwareapplication may indicate a likelihood of orthosomnia (as this mayindicate nervousness about the results of the sleepanalysis/assessment).

The biometric data may be processed using a machine-learning methodconfigured to generate the predictive indicator. Examples ofmachine-learning methods have been previously described.

In yet another embodiment, usable when the user interaction datacomprises user-derived sleep quality data, the user-derived sleepquality data may be compared to sensor-derived sleep quality data togenerate the predictive indicator. In this way, generating thepredictive indicator may include an assessment of the mismatch between asubjective perception of sleep quality (the user-derived sleep qualitydata) and objective sleep tracker data (the sensor-derived sleep qualitydata).

The user-derived sleep quality data may, for example, comprise one ormore a numerical measure or indicator of a perceived quality of sleepprovided by the individual (e.g. on a scale of 0 to 10, 1 to 10, 0 to100 or 1 to 100). The user-derived sleep quality data may comprise aplurality of such measures/indicators, e.g. representing perceivedquality for different aspects of sleep (e.g. perceived sleep depth,sleep consistency, wakefulness and so on).

Thus, as illustrated, step 220 may comprise a step 221 of obtaining,from a sleep sensor, sleep sensor data 225 responsive to one or morechanges of physiological parameters of the individual during theindividual's sleep. The sleep sensor data may, for instance, comprisebiometric data of the individual.

The sleep sensor may be external to the processing system performing themethod 200, or may be housed in a same device as the processing system.

The step 220 may also comprise a step 222 of processing the sleep sensordata to generate sensor-derived sleep quality data, being datarepresenting an objective measure of the sleep quality of theindividual. Preferably, the sensor-derived sleep quality data providessleep sensor derived predictions of the same values or measures (e.g. ona same scale) contained in the user-derived sleep quality data.Mechanisms for generating sensor-derived sleep quality data have beenpreviously described with reference to FIG. 1.

The step 220 may then perform a step 223 of comparing the user-derivedquality input data to the sensor-derived sleep quality data to generatethe predictive indicator.

The user-derived sleep quality data and the sensor-derived sleep qualitydata may be normalized with respect to one another. For instance, bothtypes of quality data may include a measure of sleep quality on a samescale for the purposes of comparison. The predictive indicator may beresponsive to a difference in the measure of sleep quality.

Step 223 may comprise determining a difference between the user-derivedquality input data and the sensor-derived quality input data, andprocessing this difference to generate the predictive indicator. Thismay include processing the difference using predetermined thresholds,comparisons to population/historic data and/or machine-learning methods.

If possible, information used to determine the user-derived sleepquality data should be collected before the individual sees (e.g. at theuser interface) any sensor-derived sleep quality data (e.g. graph, sleepscore, etc.). This is because the sleep perception of indiviudals withorthosomnia may be affected by their objective sleep data, which wouldaffect the reliabiltiy of the user-derived sleep quality data.

Proposed embodiments thereby facilitate generation of a predictiveindicator responsive to user interaction data which representsinformation obtained from a user's interaction with a softwareapplication (for sleep analysis/assessment). The user interaction datamay comprise information intentionally supplied by the user (e.g. usableto generate user-derived sleep quality data) and/or metadata concerninga user's interaction with the software application (e.g. informationabout a time, frequency and/or duration of access).

A combination of the above described approaches used for generating thepredictive indicator may be employed.

In some embodiments, a combination of user interaction data and dataderived from a sleep sensor (“sleep sensor data”) may be used togenerate the predictive indicator. In other words, the predictiveindicator may be responsive to a combination of user interaction dataand sensor-derived data.

For instance, user interaction data may comprise a frequency with whichthe individual access the software application (or certain parts of thesoftware application), and the sleep sensor data may comprise apredicted length of sleep (or time in bed)—e.g. by assessing movement ofthe individual. The predictive indicator may indicate likely orthosomniaif the user interaction data indicates an increased frequency ofchecking the software application (or certain parts thereof) and anincreased level of sleep (e.g. attempts to obtain 9 or 10 hours ofsleep). This indicates that the user is attempting to get more timeasleep, and may therefore have orthosomnia.

Other suitable examples will be apparent to the skilled person, e.g. ifthe sleep sensor data indicates increased restlessness (during sleep)accompanied by frequent access of the software application during thenight (e.g. more than 3 times during the night), indicating that theindividual is stressed about their sleep during the night.

In some examples, the computer-implemented method further comprises astep (not shown) of displaying the predictive indictor via the userinterface. This provides an individual/clinician with additionalinformation to aid them in performing a treatment/diagnostic process.

This disclosure also proposes mechanisms to perform in response to thepredictive indicator indicating that the user has, or is likely to have,orthosomnia. In particular, the disclosure proposes an approach forcontrolling the software application to modify and/or supplementinformation presented to the individual, via a user interface, duringthe sleep analysis or assessment process based on the predictiveindicator.

The purpose of these mechanisms is the reduction of an individual'sstress and anxiety related to sleep scores and the desire to achieveperceived perfect sleep.

Thus, there is proposed a computer-implemented method of controlling asoftware application running on a processor for performing a sleepanalysis or assessment process.

Various approaches for controlling the software application areenvisaged in the present disclosure if the individual is suspected oforthosomnia, e.g. if the predictive indicator indicators that the useris likely to have orthosomnia. These approaches are particularly usefulif the software application is configured to generate and display, atthe user interface, a sleep quality measure—which is derived from dataobtained from a sleep sensor (i.e. is a sensor-derived sleep qualitymeasure).

FIG. 3 illustrates a flowchart usable for describing various embodimentsof such a computer-implemented method 300. The method 300 comprisesperforming a process 200 of generating a predictive indicator thatindicates a likelihood of orthosomnia in an individual. Exampleprocesses 200 have been previously described with reference to FIG. 2.

In one example, the method 300 comprises a step 310 of controlling thesoftware application to modify the sleep quality measure (displayed atthe user interface) in response to the predictive indicator indicatingthat a likeliness of orthosomnia in the individual falls within a firstpredetermined range.

Thus, instead of simply displaying a sleep quality measure as derivedfrom sensor data, the sleep quality measure may be revised to takeaccount of likely orthosomnia of the individual, e.g. making the sleepquality measure more positive (e.g. revising a sleep quality measureupwards) to reduce anxiety or stress on the individual. This stepeffectively aims to obfuscate the sleep quality measure based on thesleep tracker analysis algorithm, to reduce the impact of the sleepquality measure data on the individual.

In another example, the method 300 comprises a step 320 of suppressingthe display of the sleep quality measure in response to the predictiveindicator indicating that a likeliness of orthosomnia falls within asecond predetermined range. Thus, a sleep quality measure may prevent asleep quality measure from being generated and presented to theindividual.

The display of the sleep quality measure may, for instance, besuppressed for a predetermined period of time (e.g. 3 days or a week)and/or during certain time period or times of the day (e.g. during atime during which the individual is supposed to be asleep).

This approach aims to address an individual's obsession with thesensor-derived sleep quality data, to reduce anxiety and/or stress aboutthe sensor-derived sleep quality measure if they are predicted to haveorthosomnia. In particular, this approach is intended to encourage theindividual to avoid behavioral habits that may lead to, or reinforceexisting, orthosomnia (i.e. frequent software application checking andthe like).

In some examples, the step of suppressing the display of the sleepquality measure may comprise removing access to, or preventing theindividual from accessing, the software application, e.g. for apredetermined period of time (e.g. 3 days or a week) and/or duringcertain time periods (e.g. during the night).

In some examples, the method 300 comprises a step 330 of (generatingand) providing, at the display, supplementary information about sleepquality measures in response to the predictive indicator indicating thata likeliness of orthosomnia falls within a third predetermined range.

The purpose of the supplementary information may be to divert theindividual's attention from a sleep quality measure and/ordecatastrophize a poor sleep quality measure.

In particularly preferable examples, the supplementary information isresponsive to a difference between user-derived sleep quality data andthe sensor-derived sleep quality data. This approach can help emphasizepotential discrepancies between the user's reported experiences andsuggesting plausible explanations for such differences.

For instance, if a difference between user-derived sleep quality dataand the sensor-derived sleep quality data is identified, thesupplementary information may generate text indicating that thesensor-derived data may be inaccurate (e.g. as it differs significantlyfrom the user-derived data). In particular, orthosomnia can relate to ascenario in which the user has a subjective feeling of high sleepquality, but the sensor-derived sleep quality data indicates that theuser had a low sleep quality. The supplementary information may provideplausible explanations for this inaccuracy (e.g. incorrect placement ofthe sleep sensor, over or under-sensitivity of the sleep sensor and soon). This approach can help assuage an individual's concerns, whilstmaintaining their confidence in the method of the present disclosure.

Accordingly, step 330 may comprise a step 331 of obtaining, from a sleepsensor, sleep sensor data 225 responsive to one or more changes ofphysiological parameters of the individual during the individual'ssleep; and a step 332 of processing the sleep sensor data to generatesensor-derived sleep quality data, being data representing an objectivemeasure of the sleep quality of the individual; and a step 333 ofgenerating the supplementary information, which comprises informationresponsive to a difference between the user-derived sleep quality data215 (e.g. obtained by process 200) and the sensor-derived sleep qualitydata. Steps 331 and 332 may be functionally equivalent to steps 221 and222 (described with reference to FIG. 2), and may be the same process(i.e. the process is only performed once).

In some examples, the supplementary information may contain informationspecific to the software application performing the sleep analysisand/or assessment and/or the sleep analysis/assessment approach itself,such as a confidence level in the sleep quality measure (e.g. in termsof %). This aids the individual in gaining confidence in the system andalgorithm.

In some examples, the supplementary information is responsive tohistoric and/or global sleep trends. This can, for instance, aid incontextualizing, decatastrophzing and/or otherwise justifying a poornight's sleep.

The supplementary information may thereby provide contextual informationoffering global normative information and/or information on the users'past history.

For instance, one example of supplementary information may be textindicating “You've had two nights of poor quality sleep this week. Whilewe all want to sleep well every night, sleep specialists don't typicallydiagnose a problem unless you have difficulty falling or staying asleeptypically three or more nights per week. Your history suggests that youtypically have healthy sleep patterns and weeks like this aren't out ofthe ordinary. In fact, 95% of healthy normal sleepers have had weeksjust like this or worse!”. It will be appreciated that the content ofthis exemplary text may be revised depending upon the specificcircumstances for an individual—for instance, text may be omitted ifirrelevant and/or values in the example text may be modified to reflecta true user's situation.

The supplementary information may also/otherwise provide text seeking todecatastrophize an individual for cognitive restructuring around thepotential impact of a poor night of sleep includes offering userencouragement. Some example text may indicate “While last night's sleepwasn't the best, last time this happened you bounced back quickly.”. Theskilled person will appreciate how the supplementary information maytherefore depend upon historic data of the individual.

Supplementary information thereby provides the opportunity for theindividual to reflect on the likely magnitude and type of impact of thepoor night of sleep is on their day.

The approaches described above make use of predetermined ranges withrespect to the predictive indicator. The nature of the predeterminedranges may depend upon the content of the predictive indicator, and caninclude binary ranges, categorical ranges and/or numeric ranges asappropriate. The predetermined ranges may be identical and/or mayoverlap one another.

FIG. 4 is a schematic diagram of a processing system 400, according toembodiments of the present disclosure. As shown, the processing system400 may include a (data) processor 460, a memory 464, and acommunication module 468. These elements may be in direct or indirectcommunication with each other, for example via one or more buses.

The processor 460 may include a central processing unit (CPU), a digitalsignal processor (DSP), an ASIC, a controller, an FPGA, another hardwaredevice, a firmware device, or any combination thereof configured toperform the operations described herein. The processor 460 may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration. In some embodiments, the processor is a distributedprocessing system, e.g. formed of a set of distributed processors.

The memory 464 may include a cache memory (e.g., a cache memory of theprocessor 460), random access memory (RAM), magnetoresistive RAM (MRAM),read-only memory (ROM), programmable read-only memory (PROM), erasableprogrammable read only memory (EPROM), electrically erasableprogrammable read only memory (EEPROM), flash memory, solid state memorydevice, hard disk drives, other forms of volatile and non-volatilememory, or a combination of different types of memory. In an embodiment,the memory 464 includes a non-transitory computer-readable medium. Thenon-transitory computer-readable medium may store instructions. Forexample, the memory 464, or non-transitory computer-readable medium mayhave program code recorded thereon, the program code includinginstructions for causing the processing system 400, or one or morecomponents of the processing system 400, particularly the processor 460,to perform the operations described herein. For example, the processingsystem 400 can execute operations of the method 700. Instructions 466may also be referred to as code or program code. The terms“instructions” and “code” should be interpreted broadly to include anytype of computer-readable statement(s). For example, the terms“instructions” and “code” may refer to one or more programs, routines,sub-routines, functions, procedures, etc. “Instructions” and “code” mayinclude a single computer-readable statement or many computer-readablestatements. The memory 464, with the code recorded thereon, may bereferred to as a computer program product.

The communication module 468 can include any electronic circuitry and/orlogic circuitry to facilitate direct or indirect communication of databetween the processing system 400, the penetration device and/or theuser interface (or other further device). In that regard, thecommunication module 468 can be an input/output (I/O) device. In someinstances, the communication module 468 facilitates direct or indirectcommunication between various elements of the processing circuit 400and/or the arrangement (FIG. 1).

It will be understood that disclosed methods are preferablycomputer-implemented methods. As such, there is also proposed theconcept of a computer program comprising computer program code forimplementing any described method when said program is run on aprocessing system, such as a computer or a set of distributedprocessors.

Different portions, lines or blocks of code of a computer programaccording to an embodiment may be executed by a processing system orcomputer to perform any herein described method. In some alternativeimplementations, the functions noted in the block diagram(s) or flowchart(s) may occur out of the order noted in the figures. For example,two blocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved.

The present disclosure proposes a computer program (product) comprisinginstructions which, when the program is executed by a computer orprocessing system, cause the computer or processing system to carry out(the steps of) any herein described method. The computer program(product) may be stored on a non-transitory computer readable medium.

Similarly, there is also proposed a computer-readable (storage) mediumcomprising instructions which, when executed by a computer or processingsystem, cause the computer or processing system to carry out (the stepsof) any herein described method. There is also proposedcomputer-readable data carrier having stored thereon the computerprogram (product) previously described. There is also proposed a datacarrier signal carrying the computer program (product) previouslydescribed.

Variations to the disclosed embodiments can be understood and effectedby those skilled in the art in practicing the claimed invention, from astudy of the drawings, the disclosure and the appended claims. In theclaims, the word “comprising” does not exclude other elements or steps,and the indefinite article “a” or “an” does not exclude a plurality. Asingle processor or other unit may fulfill the functions of severalitems recited in the claims. The mere fact that certain measures arerecited in mutually different dependent claims does not indicate that acombination of these measures cannot be used to advantage. A computerprogram may be stored/distributed on a suitable medium, such as anoptical storage medium or a solid-state medium supplied together with oras part of other hardware, but may also be distributed in other forms,such as via the Internet or other wired or wireless telecommunicationsystems. If the term “adapted to” is used in the claims or description,it is noted the term “adapted to” is intended to be equivalent to theterm “configured to”. Any reference signs in the claims should not beconstrued as limiting the scope.

1. A computer-implemented method for generating a predictive indicatorthat indicates a likelihood of orthosomnia in an individual, thecomputer-implemented method comprising: obtaining user interaction data,being data responsive to the individual's interactions, via a userinterface for a processor, with a software application running on theprocessor for performing a sleep analysis or assessment process; andprocessing the obtained user interaction data to generate the predictiveindicator that indicates a likelihood of orthosomnia in the individual.2. The computer-implemented method of claim 1, wherein the userinteraction data comprises access data responsive to the individualaccessing and/or opening the software application.
 3. Thecomputer-implemented method of claim 2, wherein the access datacomprises one or more measures of accessing and/or opening frequencyand/or duration of access.
 4. The computer-implemented method of claim2, wherein the step of processing the obtained user interaction datacomprises: obtaining population access data, being data that isresponsive to population trends of other individual's interactions withthe software application running on the processor and/or one or moreother versions of the software application running on one or more otherprocessors; and comparing the access data to the population access datato generate the predictive indicator.
 5. The computer-implemented methodof claim 2, wherein the access data comprises time data indicating atime at which the individual interacts with the software application,and wherein the step of processing the obtained user interaction datacomprises: obtaining, from a biometric sensor, biometric data of theuser, the biometric data being responsive to changes in one or morephysiological parameters of the individual at or during a time at whichthe individual accesses the and/or opens the software application, asindicated by the time data; and processing the biometric data togenerate the predictive indicator.
 6. The computer-implemented method ofclaim 5, wherein the one or more physiological parameters of theindividual comprise a physiological parameter responsive to an autonomicresponse of the individual, such as a heartrate, sweating, atemperature, a respiratory rate, skin color, eye movement and/or an eyedilation.
 7. The computer-implemented method of claim 2, wherein thestep of processing the obtained user interaction data comprises:obtaining historic access data, being data responsive to the individualshistoric interactions, via the user interface, with the softwareapplication; and comparing the access data to the historic access datato generate the predictive indicator.
 8. The computer-implemented methodof claim 1, wherein: the step of obtaining the user interaction datacomprises obtaining, via the user interface, user-derived sleep qualitydata representing a subjective perception of sleep quality of theindividual; and the step of processing the obtained user interactiondata comprises: obtaining, from a sleep sensor, sleep sensor dataresponsive to one or more changes of physiological parameters of theindividual during the individual's sleep; processing the sleep sensordata to generate sensor-derived sleep quality data, being datarepresenting an objective measure of the sleep quality of theindividual; and comparing the user-derived quality input data to thesensor-derived sleep quality data to generate the predictive indicator.9. A computer-implemented method of controlling a software applicationrunning on a processor for performing a sleep analysis or assessmentprocess, the computer-implemented method comprising: generating apredictive indicator that indicates a likelihood of orthosomnia in anindividual by performing the method of claim 1; and controlling thesoftware application to modify and/or supplement information presentedto the individual, via the user interface, during the sleep analysis orassessment process based on the predictive indicator.
 10. Thecomputer-implemented method of claim 9, wherein the software applicationis configured to generate and display, at the user interface, a sleepquality measure during the sleep analysis or assessment process, and thestep of controlling the software application comprises at least one of:modifying the sleep quality measure in response to the predictiveindicator indicating that a likeliness of orthosomnia in the individualfalls within a first predetermined range; suppressing the display of thesleep quality measure in response to the predictive indicator indicatingthat a likeliness of orthosomnia falls within a second predeterminedrange; and/or providing, at the display, supplementary information aboutsleep quality measures in response to the predictive indicatorindicating that a likeliness of orthosomnia falls within a thirdpredetermined range.
 11. The computer-implemented method of claim 10,wherein: the step of obtaining the user interaction data comprisesobtaining, via the user interface, user-derived sleep quality datarepresenting a subjective perception of sleep quality of the individual:the computer-implemented method further comprises: obtaining, from asleep sensor, sleep sensor data responsive to one or more changes ofphysiological parameters of the individual during the individual'ssleep; and processing the sleep sensor data to generate sensor-derivedsleep quality data, being data representing an objective measure of thesleep quality of the individual, wherein the supplementary informationcomprises information responsive to a difference between theuser-derived sleep quality data and the sensor-derived sleep qualitydata.
 12. The computer-implemented method of claim 1, wherein thecomputer-implemented method is performed by the software application.13. The computer-implemented method of claim 1, wherein the predictiveindicator comprise a binary indicator indicating a prediction of whetheror not the individual has orthosomnia.
 14. A computer program productcomprising computer program code means which, when executed on acomputing device having a processing system, cause the processing systemto perform all of the steps of the method according to claim
 1. 15. Aprocessing system for generating a predictive indicator that indicates alikelihood of orthosomnia in an individual, the processing system beingconfigured to: obtain user interaction data, being data responsive tothe individual's interactions, via a user interface for a processor,with a software application running on the processor for performing asleep analysis or assessment process; and process the obtained userinteraction data to generate the predictive indicator that indicates alikelihood of orthosomnia in the individual.